In Heterogeneous Networks (HetNets), Integrating Device-to-Device communication (D2D) techniques presents as a promising solution for improving system performance by offloading traffic from heavily loaded macro cell (...In Heterogeneous Networks (HetNets), Integrating Device-to-Device communication (D2D) techniques presents as a promising solution for improving system performance by offloading traffic from heavily loaded macro cell (MC) to small cells (SCs). For instance, D2D can be used to offload traffic from heavily-loaded cells to light-loaded small cells. However, offloading new users may result in an unfair load distribution among small cells and consequently may affect the quality of service of some users. To achieve better performance and reduce blocking probability load balancing among small cells should be considered when we offload traffic from macro to small cells. In this paper, we consider a centralized offloaded relay selection scheme, in which a cellular provider can decide whether users can assist each other to relay their traffic to small cells. We propose a joint user-relay selection with dynamic load balancing scheme based on D2D communications using the Kuhn-Munkres (K-M) method. The offloading process considers the load from MC to SCs and among SCs. Compared to previous works, our simulation results show that the proposed scheme increases the number of admitted users in the system, and achieves a higher load balancing fairness index among small cells. Also, our scheme achieves a higher rate fairness index among users by adjusting the signal to interference plus noise ratio (SINR) threshold.展开更多
This paper aims to reduce the communication cost of the distributed learning algorithm for stochastic configuration networks (SCNs), in which information exchange between the learning agents is conducted only at a tri...This paper aims to reduce the communication cost of the distributed learning algorithm for stochastic configuration networks (SCNs), in which information exchange between the learning agents is conducted only at a trigger time. For this purpose, we propose the communication-censored distributed learning algorithm for SCN, namely ADMMM-SCN-ET, by introducing the event-triggered communication mechanism to the alternating direction method of multipliers (ADMM). To avoid unnecessary information transmissions, each learning agent is equipped with a trigger function. Only if the event-trigger error exceeds a specified threshold and meets the trigger condition, the agent will transmit the variable information to its neighbors and update its state in time. The simulation results show that the proposed algorithm can effectively reduce the communication cost for training decentralized SCNs and save communication resources.展开更多
Individuals exchange information,experience and strategy based on communication.Communication is the basis for individuals to form swarms and the bridge of swarms to realize cooperative control.In this paper,the multi...Individuals exchange information,experience and strategy based on communication.Communication is the basis for individuals to form swarms and the bridge of swarms to realize cooperative control.In this paper,the multirobot swarm and its cooperative control and communication methods are reviewed,and we summarize these methods from the task,control,and perception levels.Based on the research,the cooperative control and communication methods of intelligent swarms are divided into the following four categories:task assignment based methods(divided into market-based methods and alliance based methods),bio-inspired methods(divided into biochemical information inspired methods,vision based methods and self-organization based methods),distributed sensor fusion and reinforcement learning based methods,and we briefly define each method and introduce its basic ideas.Based on WOS database,we divide the development of each method into several stages according to the time distribution of the literature,and outline the main research content of each stage.Finally,we discuss the communication problems of intelligent swarms and the key issues,challenges and future work of each method.展开更多
文摘In Heterogeneous Networks (HetNets), Integrating Device-to-Device communication (D2D) techniques presents as a promising solution for improving system performance by offloading traffic from heavily loaded macro cell (MC) to small cells (SCs). For instance, D2D can be used to offload traffic from heavily-loaded cells to light-loaded small cells. However, offloading new users may result in an unfair load distribution among small cells and consequently may affect the quality of service of some users. To achieve better performance and reduce blocking probability load balancing among small cells should be considered when we offload traffic from macro to small cells. In this paper, we consider a centralized offloaded relay selection scheme, in which a cellular provider can decide whether users can assist each other to relay their traffic to small cells. We propose a joint user-relay selection with dynamic load balancing scheme based on D2D communications using the Kuhn-Munkres (K-M) method. The offloading process considers the load from MC to SCs and among SCs. Compared to previous works, our simulation results show that the proposed scheme increases the number of admitted users in the system, and achieves a higher load balancing fairness index among small cells. Also, our scheme achieves a higher rate fairness index among users by adjusting the signal to interference plus noise ratio (SINR) threshold.
文摘This paper aims to reduce the communication cost of the distributed learning algorithm for stochastic configuration networks (SCNs), in which information exchange between the learning agents is conducted only at a trigger time. For this purpose, we propose the communication-censored distributed learning algorithm for SCN, namely ADMMM-SCN-ET, by introducing the event-triggered communication mechanism to the alternating direction method of multipliers (ADMM). To avoid unnecessary information transmissions, each learning agent is equipped with a trigger function. Only if the event-trigger error exceeds a specified threshold and meets the trigger condition, the agent will transmit the variable information to its neighbors and update its state in time. The simulation results show that the proposed algorithm can effectively reduce the communication cost for training decentralized SCNs and save communication resources.
基金supported by National Natural Science Foundation of China(No.61803383).
文摘Individuals exchange information,experience and strategy based on communication.Communication is the basis for individuals to form swarms and the bridge of swarms to realize cooperative control.In this paper,the multirobot swarm and its cooperative control and communication methods are reviewed,and we summarize these methods from the task,control,and perception levels.Based on the research,the cooperative control and communication methods of intelligent swarms are divided into the following four categories:task assignment based methods(divided into market-based methods and alliance based methods),bio-inspired methods(divided into biochemical information inspired methods,vision based methods and self-organization based methods),distributed sensor fusion and reinforcement learning based methods,and we briefly define each method and introduce its basic ideas.Based on WOS database,we divide the development of each method into several stages according to the time distribution of the literature,and outline the main research content of each stage.Finally,we discuss the communication problems of intelligent swarms and the key issues,challenges and future work of each method.
